Finite-Sample Bounds for Adaptive Inverse Reinforcement Learning using Passive Langevin Dynamics

This paper provides a finite-sample analysis of a passive stochastic gradient Langevin dynamics algorithm (PSGLD) designed to achieve adaptive inverse reinforcement learning (IRL). By passive, we mean that the noisy gradients available to the PSGLD algorithm (inverse learning process) are evaluated...

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Bibliographische Detailangaben

Hauptverfasser:	Snow, Luke, Krishnamurthy, Vikram
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Learning Statistics - Machine Learning
Online-Zugang:	Volltext bestellen
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